LangChain + Pinecone + OpenAI
RAG / AI SearchThe canonical RAG stack. Embeddings, vector storage, and LLM orchestration for AI-powered search.
Components
Why This Stack
OpenAI provides both the embedding model (text-embedding-3-small) and the completion model (GPT-4o). Pinecone stores and queries embeddings with low latency at scale. LangChain ties them together with document loaders, text splitters, retrieval chains, and prompt templates.
Integration Notes
Use LangChain's PineconeVectorStore with OpenAI embeddings for zero-config vector setup
Split documents with RecursiveCharacterTextSplitter (chunk size 1000, overlap 200) as a starting point
Use LangChain's RetrievalQA chain for simple RAG, or LCEL for more control
Set Pinecone index dimension to 1536 for text-embedding-3-small or 3072 for text-embedding-3-large
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